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Summary of Changes

Hello @niveditasing, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refines the World Bank dataset processing script by integrating external CSV files for dynamic country code management. It enables the system to correctly map country codes to standardized Data Commons IDs and to filter out irrelevant country data, thereby enhancing the precision and relevance of the processed datasets. The changes also include minor code formatting improvements for better readability.

Highlights

  • Country Code Mapping and Filtering: Introduced functionality to map World Bank country codes to Data Commons IDs (DCIDs) using a places.csv file and to ignore specific country codes using a skip_places.csv file during data processing.
  • Data Processing Logic Enhancement: Modified the transform_worldbank_csv function to incorporate the new DCID mapping and country code skipping logic, ensuring more accurate and filtered data output.
  • Code Readability and Maintenance: Applied minor formatting changes across the script, including multi-line arguments for function calls and list definitions, to improve overall code readability and maintainability.

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Code Review

This pull request introduces logic to map and filter country codes using external CSV files (places.csv and skip_places.csv) when processing World Bank datasets. While the intention to make the processing more flexible is good, I've found a critical bug in the implementation of the country code lookup that will prevent it from working as expected. Additionally, there are some areas for improvement regarding Python best practices, such as avoiding file I/O at the module level and handling exceptions more specifically to improve robustness and debuggability. My review includes detailed comments and suggestions to address these points.

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